计算机应用与软件2024，Vol.41Issue(8)：359-366,8.DOI:10.3969/j.issn.1000-386x.2024.08.051
一种卷积神经网络结合特征融合的网络入侵检测方法
A NETWORK INTRUSION DETEDTION METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK AND FEATURE FUSION
摘要
Abstract
In order to solve the problems of few attack features,data imbalance and slow convergence in traditional network intrusion detection methods,this paper proposes an intrusion detection method based on convolutional neural network and feature fusion.This method converted the traffic data into a gray image to extract its texture features,and fused the texture features with network traffic features to increase the amount of attack characteristics.The Borderline-SMOTE method was used to balance the UNSW-NB15 data set.The greedy layer-wise training method was used to optimize the convolutional neural network model to improve the convergence speed of the model.Experiments show that the performance of this method is better than other detection methods,and the accuracy rate can be increased to 96.38％.关键词
入侵检测/特征融合/逐层贪婪训练/卷积神经网络/Borderline-SMOTEKey words
Intrusion detection/Feature fusion/Greedy layer-wise training/Convolutional neural networks/Borderline-SMOTE分类
信息技术与安全科学引用本文复制引用
王雪妍,温蜜,李晋国,熊赟..一种卷积神经网络结合特征融合的网络入侵检测方法[J].计算机应用与软件,2024,41(8):359-366,8.基金项目
国家自然科学基金项目(61872230,61802248,61802249) （61872230,61802248,61802249）
上海市2019年度"科技创新行动计划"高新技术领域项目(19511103700) （19511103700）
上海市科委项目(20020500600). （20020500600）
